Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 65160

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 71469

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-21T21:51:54+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

COVID-19 対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	132670
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	130859	0
dateAnnounced	0	1.00	10	10	0	298	0
gender	31229	0.76	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	128569	0.03	8	23	0	8	0
notes	71090	0.46	1	270	0	58667	1
mhlwPatientNumber	132221	0.00	1	11	0	434	0
prefecturePatientNumber	27696	0.79	5	20	0	104965	0
prefectureSourceURL	101279	0.24	5	224	0	3453	0
residence	39505	0.70	1	38	0	1427	0
sourceURL	1130	0.99	1	239	0	8570	0
relatedPatients	121086	0.09	2	259	0	6919	0
knownCluster	130156	0.02	3	88	0	232	0
detectedCityTown	104961	0.21	2	22	0	667	0
cityPrefectureNumber	105260	0.21	1	34	0	27401	2
citySourceURL	120671	0.09	9	317	0	3671	0
deceasedDate	130728	0.01	10	10	0	248	0
deceasedReportedDate	131450	0.01	10	62	0	207	0
deathSourceURL	131595	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 130858, FAL: 1812
charterFlightPassenger	132656	0	1.00	TRU: 14
cruisePassengerDisembarked	132659	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	28.81	24.56	-1	0	30	50	100	▇▇▅▃▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	129429
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	129429	0
knownCluster	126965	0.02	3	88	0	229	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-21	2020-08-27	295
deceasedDate	129050	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	129100	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	29580	0.77	FALSE	2	M: 55931, F: 43918
patientStatus	126918	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 29665, 20: 27013, 30: 17323, 40: 14506
pcode	0	1.00	FALSE	47	13: 37355, 27: 17159, 14: 11199, 23: 8686
pref	0	1.00	FALSE	47	東京都: 37355, 大阪府: 17159, 神奈川: 11199, 愛知県: 8686
region	0	1.00	FALSE	8	関東地: 65160, 近畿地: 27052, 中部地: 13545, 九州地: 12209
広域圏	10879	0.92	FALSE	8	首都圏: 65468, 近畿圏: 26347, 中部圏: 12069, 九州圏: 8237
通俗的区分	0	1.00	FALSE	11	関東: 65160, 関西: 26347, 東海: 11478, 九州: 8237
fct_pref	0	1.00	FALSE	47	Tok: 37355, Osa: 17159, Kan: 11199, Aic: 8686

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 129429
charterFlightPassenger	129422	0	1.00	TRU: 7
cruisePassengerDisembarked	129418	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 126965, TRU: 2464

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.02）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.02）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

年代別

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5")),
                ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "0", "10", "20", "30", "40",
                                                  "50", "60", "70", "80", "90",
                                                  "100")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region),
                ageBracket = forcats::fct_rev(ageBracket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年代比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5")),
                ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "0", "10", "20", "30", "40",
                                                  "50", "60", "70", "80", "90",
                                                  "100")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region),
                ageBracket = forcats::fct_rev(ageBracket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年代不明者をのぞく年代比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5")),
                ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "0", "10", "20", "30", "40",
                                                  "50", "60", "70", "80", "90",
                                                  "100")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region),
                ageBracket = forcats::fct_rev(ageBracket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年代比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5")),
                ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "0", "10", "20", "30", "40",
                                                  "50", "60", "70", "80", "90",
                                                  "100")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region),
                ageBracket = forcats::fct_rev(ageBracket)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年代不明者をのぞく年代比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 251.7282 216.2084 252.8128 268.9388 285.7971 300.2218 275.7992 288.1108
##  [9] 282.0335 301.1716 308.6192 316.1823 320.6539 321.6421
## 
## $北海道地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 240.0116 233.8092
## 45.71429 202.4905 195.2287
## 45.85714 235.4360 226.2373
## 46.00000 250.7460 241.1153
## 46.14286 266.5792 256.4059
## 46.28571 279.8738 269.1022
## 46.42857 253.9746 242.4214
## 46.57143 262.6334 249.1465
## 46.71429 254.1453 239.3822
## 46.85714 270.4638 254.2081
## 47.00000 276.0838 258.8606
## 47.14286 281.6585 263.3828
## 47.28571 284.0479 264.6698
## 47.42857 282.7515 262.1640
## 
## $北海道地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 263.4447 269.6471
## 45.71429 229.9263 237.1881
## 45.85714 270.1896 279.3884
## 46.00000 287.1317 296.7624
## 46.14286 305.0149 315.1882
## 46.28571 320.5698 331.3414
## 46.42857 297.6238 309.1771
## 46.57143 313.5881 327.0750
## 46.71429 309.9217 324.6848
## 46.85714 331.8793 348.1351
## 47.00000 341.1547 358.3779
## 47.14286 350.7060 368.9817
## 47.28571 357.2599 376.6379
## 47.42857 360.5327 381.1202
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 27.48614 32.24134 36.67737 34.39690 33.56829 34.42678 36.40946 33.50052
##  [9] 35.30407 34.09067 35.16165 34.62401 35.12824 35.03106
## 
## $東北地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 20.58284 16.92846
## 45.71429 25.00315 21.17148
## 45.85714 29.35850 25.48413
## 46.00000 27.05037 23.16135
## 46.14286 25.97803 21.95999
## 46.28571 26.83008 22.80864
## 46.42857 28.57601 24.42924
## 46.57143 25.28220 20.93169
## 46.71429 26.77628 22.26194
## 46.85714 25.49401 20.94322
## 47.00000 26.42226 21.79591
## 47.14286 25.74977 21.05203
## 47.28571 26.15773 21.40904
## 47.42857 25.90750 21.07778
## 
## $東北地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 34.38944 38.04382
## 45.71429 39.47954 43.31121
## 45.85714 43.99623 47.87061
## 46.00000 41.74343 45.63245
## 46.14286 41.15855 45.17659
## 46.28571 42.02347 46.04492
## 46.42857 44.24290 48.38967
## 46.57143 41.71883 46.06935
## 46.71429 43.83186 48.34620
## 46.85714 42.68732 47.23811
## 47.00000 43.90105 48.52740
## 47.14286 43.49825 48.19599
## 47.28571 44.09875 48.84744
## 47.42857 44.15462 48.98434
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1]  894.8970  770.4655  964.5534 1083.3938 1161.9245 1140.3311 1192.4062
##  [8] 1002.5053  889.1740 1081.7174 1199.0414 1276.0833 1253.0284 1303.6688
## 
## $関東地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143  826.0414  789.5915
## 45.71429  684.3399  638.7478
## 45.85714  869.9382  819.8519
## 46.00000  981.2618  927.1964
## 46.14286 1053.0377  995.3965
## 46.28571 1025.3087  964.4195
## 46.42857 1071.7652 1007.9017
## 46.57143  868.2510  797.1811
## 46.71429  745.0233  668.7146
## 46.85714  929.9048  849.5401
## 47.00000 1040.1935  956.1046
## 47.14286 1110.7379 1023.2093
## 47.28571 1081.6534  990.9329
## 47.42857 1126.6766 1032.9827
## 
## $関東地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143  963.7526 1000.2025
## 45.71429  856.5911  902.1832
## 45.85714 1059.1686 1109.2549
## 46.00000 1185.5258 1239.5912
## 46.14286 1270.8113 1328.4525
## 46.28571 1255.3534 1316.2426
## 46.42857 1313.0472 1376.9107
## 46.57143 1136.7596 1207.8296
## 46.71429 1033.3247 1109.6334
## 46.85714 1233.5299 1313.8946
## 47.00000 1357.8892 1441.9781
## 47.14286 1441.4288 1528.9574
## 47.28571 1424.4035 1515.1239
## 47.42857 1480.6609 1574.3549
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 298.4616 272.5776 345.0406 377.5421 398.3195 392.2744 402.6985 372.9823
##  [9] 353.0664 408.8216 433.8293 449.8160 445.1648 453.1854
## 
## $中部地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 276.8454 265.4024
## 45.71429 245.2538 230.7895
## 45.85714 313.0107 296.0551
## 46.00000 341.4140 322.2889
## 46.14286 358.5129 337.4405
## 46.28571 349.1016 326.2473
## 46.42857 356.4036 331.8965
## 46.57143 319.5348 291.2413
## 46.71429 294.2950 263.1834
## 46.85714 345.1702 311.4752
## 47.00000 365.6462 329.5522
## 47.14286 377.3843 339.0412
## 47.28571 368.7201 328.2527
## 47.42857 372.9281 330.4425
## 
## $中部地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 320.0779 331.5209
## 45.71429 299.9014 314.3657
## 45.85714 377.0705 394.0261
## 46.00000 413.6702 432.7953
## 46.14286 438.1261 459.1985
## 46.28571 435.4473 458.3016
## 46.42857 448.9934 473.5004
## 46.57143 426.4299 454.7233
## 46.71429 411.8378 442.9494
## 46.85714 472.4731 506.1681
## 47.00000 502.0124 538.1063
## 47.14286 522.2478 560.5909
## 47.28571 521.6095 562.0769
## 47.42857 533.4426 575.9282
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 593.2823 398.3326 630.0270 649.0183 711.3099 731.8206 787.2753 733.5543
##  [9] 585.4600 761.4675 775.8944 823.2145 838.7954 880.9218
## 
## $近畿地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 555.0315 534.7828
## 45.71429 354.5928 331.4384
## 45.85714 583.2903 558.5494
## 46.00000 599.4656 573.2340
## 46.14286 659.0928 631.4508
## 46.28571 677.0686 648.0847
## 46.42857 730.1007 699.8343
## 46.57143 660.4696 621.7809
## 46.71429 505.4415 463.0822
## 46.85714 676.3643 631.3133
## 47.00000 685.9936 638.4029
## 47.14286 728.7594 678.7579
## 47.28571 739.9959 687.6946
## 47.42857 777.9609 723.4567
## 
## $近畿地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 631.5331  651.7819
## 45.71429 442.0723  465.2267
## 45.85714 676.7636  701.5045
## 46.00000 698.5710  724.8026
## 46.14286 763.5270  791.1690
## 46.28571 786.5725  815.5565
## 46.42857 844.4499  874.7162
## 46.57143 806.6391  845.3278
## 46.71429 665.4786  707.8378
## 46.85714 846.5708  891.6218
## 47.00000 865.7952  913.3859
## 47.14286 917.6695  967.6710
## 47.28571 937.5950  989.8963
## 47.42857 983.8828 1038.3870
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 39.3428 39.3428 39.3428 39.3428 39.3428 39.3428 39.3428 39.3428 39.3428
## [10] 39.3428 39.3428 39.3428 39.3428 39.3428
## 
## $中国地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 29.86880 24.85357
## 45.71429 29.44668 24.20799
## 45.85714 29.04185 23.58885
## 46.00000 28.65233 22.99313
## 46.14286 28.27652 22.41838
## 46.28571 27.91305 21.86251
## 46.42857 27.56080 21.32378
## 46.57143 27.21877 20.80069
## 46.71429 26.88613 20.29197
## 46.85714 26.56215 19.79647
## 47.00000 26.24617 19.31324
## 47.14286 25.93765 18.84139
## 47.28571 25.63606 18.38015
## 47.42857 25.34098 17.92885
## 
## $中国地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 48.81681 53.83204
## 45.71429 49.23893 54.47762
## 45.85714 49.64376 55.09676
## 46.00000 50.03328 55.69247
## 46.14286 50.40909 56.26723
## 46.28571 50.77256 56.82310
## 46.42857 51.12481 57.36183
## 46.57143 51.46684 57.88492
## 46.71429 51.79948 58.39364
## 46.85714 52.12346 58.88913
## 47.00000 52.43943 59.37237
## 47.14286 52.74796 59.84422
## 47.28571 53.04954 60.30545
## 47.42857 53.34463 60.75675
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 17.66554 15.56563 15.56563 15.56563 15.56563 15.56563 15.56563 15.56563
##  [9] 15.56563 15.56563 15.56563 15.56563 15.56563 15.56563
## 
## $四国地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 13.951667 11.985662
## 45.71429 11.460157  9.286848
## 45.85714 11.286259  9.020894
## 46.00000 11.119157  8.765333
## 46.14286 10.958111  8.519035
## 46.28571 10.802507  8.281060
## 46.42857 10.651829  8.050617
## 46.57143 10.505635  7.827033
## 46.71429 10.363548  7.609730
## 46.85714 10.225240  7.398206
## 47.00000 10.090425  7.192024
## 47.14286  9.958850  6.990798
## 47.28571  9.830294  6.794187
## 47.42857  9.704556  6.601888
## 
## $四国地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 21.37941 23.34542
## 45.71429 19.67111 21.84442
## 45.85714 19.84501 22.11038
## 46.00000 20.01211 22.36594
## 46.14286 20.17316 22.61223
## 46.28571 20.32876 22.85021
## 46.42857 20.47944 23.08065
## 46.57143 20.62564 23.30424
## 46.71429 20.76772 23.52154
## 46.85714 20.90603 23.73306
## 47.00000 21.04084 23.93925
## 47.14286 21.17242 24.14047
## 47.28571 21.30098 24.33708
## 47.42857 21.42671 24.52938
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 118.2846 112.5091 116.7096 135.4453 137.5724 147.2420 140.7183 142.6814
##  [9] 137.8639 144.4817 157.5538 156.2541 162.1668 159.4047
## 
## $九州地方$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%      95%
## 45.57143  96.26459 84.60791
## 45.71429  85.75021 71.58490
## 45.85714  86.41200 70.37344
## 46.00000 103.78272 87.02159
## 46.14286 103.82561 85.96113
## 46.28571 109.56414 89.61866
## 46.42857 100.45361 79.13879
## 46.57143  96.86467 72.61073
## 46.71429  88.07523 61.71867
## 46.85714  90.32779 61.66043
## 47.00000 100.23270 69.88877
## 47.14286  96.11212 64.27487
## 47.28571  98.64880 65.02440
## 47.42857  92.67773 57.35462
## 
## $九州地方$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 140.3046 151.9613
## 45.71429 139.2680 153.4333
## 45.85714 147.0072 163.0457
## 46.00000 167.1078 183.8690
## 46.14286 171.3192 189.1837
## 46.28571 184.9200 204.8654
## 46.42857 180.9829 202.2977
## 46.57143 188.4982 212.7522
## 46.71429 187.6527 214.0092
## 46.85714 198.6357 227.3030
## 47.00000 214.8748 245.2187
## 47.14286 216.3961 248.2334
## 47.28571 225.6848 259.3092
## 47.42857 226.1316 261.4547

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 251.7282 216.2084 252.8128 268.9388 285.7971 300.2218 275.7992 288.1108
##  [9] 282.0335 301.1716 308.6192 316.1823 320.6539 321.6421
## 
## $北海道$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 240.0116 233.8092
## 45.71429 202.4905 195.2287
## 45.85714 235.4360 226.2373
## 46.00000 250.7460 241.1153
## 46.14286 266.5792 256.4059
## 46.28571 279.8738 269.1022
## 46.42857 253.9746 242.4214
## 46.57143 262.6334 249.1465
## 46.71429 254.1453 239.3822
## 46.85714 270.4638 254.2081
## 47.00000 276.0838 258.8606
## 47.14286 281.6585 263.3828
## 47.28571 284.0479 264.6698
## 47.42857 282.7515 262.1640
## 
## $北海道$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 263.4447 269.6471
## 45.71429 229.9263 237.1881
## 45.85714 270.1896 279.3884
## 46.00000 287.1317 296.7624
## 46.14286 305.0149 315.1882
## 46.28571 320.5698 331.3414
## 46.42857 297.6238 309.1771
## 46.57143 313.5881 327.0750
## 46.71429 309.9217 324.6848
## 46.85714 331.8793 348.1351
## 47.00000 341.1547 358.3779
## 47.14286 350.7060 368.9817
## 47.28571 357.2599 376.6379
## 47.42857 360.5327 381.1202
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] -0.15327280  0.26896324  0.03578019  0.16455729  0.09343916  0.13271469
##  [7]  0.11102448  0.12300306  0.11638780  0.12004113  0.11802355  0.11913777
## [13]  0.11852243  0.11886226
## 
## $青森県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 -2.686739 -4.027876
## 45.71429 -2.705206 -4.279636
## 45.85714 -3.019297 -4.636557
## 46.00000 -3.108009 -4.840401
## 46.14286 -3.303553 -5.101812
## 46.28571 -3.424552 -5.307655
## 46.42857 -3.577649 -5.530315
## 46.57143 -3.704084 -5.730021
## 46.71429 -3.838069 -5.931433
## 46.85714 -3.961119 -6.121554
## 47.00000 -4.084243 -6.308788
## 47.14286 -4.201796 -6.489160
## 47.28571 -4.317392 -6.665623
## 47.42857 -4.429403 -6.837109
## 
## $青森県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 2.380194 3.721330
## 45.71429 3.243133 4.817563
## 45.85714 3.090857 4.708118
## 46.00000 3.437124 5.169516
## 46.14286 3.490432 5.288691
## 46.28571 3.689981 5.573084
## 46.42857 3.799698 5.752363
## 46.57143 3.950090 5.976027
## 46.71429 4.070845 6.164208
## 46.85714 4.201201 6.361636
## 47.00000 4.320290 6.544836
## 47.14286 4.440071 6.727435
## 47.28571 4.554437 6.902668
## 47.42857 4.667127 7.074833
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 14.93995 11.51223 12.56860 13.33495 13.95949 14.46848 14.88329 15.22136
##  [9] 15.49686 15.72140 15.90439 16.05352 16.17505 16.27410
## 
## $岩手県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 13.66051 12.983211
## 45.71429 10.05874  9.289306
## 45.85714 10.96430 10.115025
## 46.00000 11.72756 10.876664
## 46.14286 12.32619 11.461576
## 46.28571 12.77565 11.879518
## 46.42857 13.09661 12.150794
## 46.57143 13.31158 12.300609
## 46.71429 13.44197 12.354173
## 46.85714 13.50634 12.333753
## 47.00000 13.51990 12.257625
## 47.14286 13.49472 12.140180
## 47.28571 13.44024 11.992513
## 47.42857 13.36374 11.823082
## 
## $岩手県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 16.21940 16.89669
## 45.71429 12.96572 13.73516
## 45.85714 14.17291 15.02218
## 46.00000 14.94233 15.79323
## 46.14286 15.59279 16.45741
## 46.28571 16.16132 17.05745
## 46.42857 16.66998 17.61580
## 46.57143 17.13113 18.14210
## 46.71429 17.55176 18.63956
## 46.85714 17.93646 19.10904
## 47.00000 18.28887 19.55115
## 47.14286 18.61231 19.96685
## 47.28571 18.90987 20.35759
## 47.42857 19.18447 20.72512
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 16.23011 16.16054 17.15757 15.68918 17.23439 15.89766 16.86429 16.31667
##  [9] 16.48243 16.60826 16.30248 16.68071 16.31649 16.60940
## 
## $宮城県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%      95%
## 45.57143 11.123075 8.419573
## 45.71429 10.938520 8.174148
## 45.85714 11.720145 8.841745
## 46.00000 10.222945 7.329298
## 46.14286 11.504219 8.470849
## 46.28571 10.134485 7.083646
## 46.42857 10.910593 7.758896
## 46.57143 10.292351 7.103269
## 46.71429 10.342251 7.091836
## 46.85714 10.356326 7.046750
## 47.00000  9.969804 6.617489
## 47.14286 10.221566 6.802301
## 47.28571  9.782365 6.323408
## 47.42857  9.959002 6.438491
## 
## $宮城県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 21.33715 24.04065
## 45.71429 21.38256 24.14694
## 45.85714 22.59500 25.47340
## 46.00000 21.15541 24.04905
## 46.14286 22.96456 25.99794
## 46.28571 21.66083 24.71167
## 46.42857 22.81799 25.96968
## 46.57143 22.34099 25.53007
## 46.71429 22.62261 25.87303
## 46.85714 22.86020 26.16978
## 47.00000 22.63516 25.98747
## 47.14286 23.13986 26.55912
## 47.28571 22.85061 26.30957
## 47.42857 23.25981 26.78032
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 0.3782928 0.4147393 0.4147393 0.4147393 0.4147393 0.4147393 0.4147393
##  [8] 0.4147393 0.4147393 0.4147393 0.4147393 0.4147393 0.4147393 0.4147393
## 
## $秋田県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%       95%
## 45.57143 -0.8487202 -1.498262
## 45.71429 -0.8242107 -1.480071
## 45.85714 -0.8255893 -1.482180
## 46.00000 -0.8269664 -1.484286
## 46.14286 -0.8283419 -1.486389
## 46.28571 -0.8297159 -1.488491
## 46.42857 -0.8310884 -1.490590
## 46.57143 -0.8324593 -1.492686
## 46.71429 -0.8338288 -1.494781
## 46.85714 -0.8351968 -1.496873
## 47.00000 -0.8365632 -1.498963
## 47.14286 -0.8379282 -1.501050
## 47.28571 -0.8392917 -1.503136
## 47.42857 -0.8406537 -1.505219
## 
## $秋田県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 1.605306 2.254847
## 45.71429 1.653689 2.309550
## 45.85714 1.655068 2.311658
## 46.00000 1.656445 2.313764
## 46.14286 1.657820 2.315868
## 46.28571 1.659194 2.317969
## 46.42857 1.660567 2.320068
## 46.57143 1.661938 2.322165
## 46.71429 1.663307 2.324259
## 46.85714 1.664675 2.326351
## 47.00000 1.666042 2.328441
## 47.14286 1.667407 2.330529
## 47.28571 1.668770 2.332614
## 47.42857 1.670132 2.334697
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 1.7246482 1.6711177 1.7810364 1.2741054 0.6725527 1.1662139 1.3481666
##  [8] 0.9524565 0.8474411 0.7477133 0.6543605 0.5682099 0.4898478 0.4196417
## 
## $山形県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                  80%        95%
## 45.57143  0.72412454  0.1944793
## 45.71429  0.65500095  0.1171012
## 45.85714  0.74082981  0.1901777
## 46.00000  0.20244493 -0.3648579
## 46.14286 -0.43598331 -1.0228068
## 46.28571  0.01759628 -0.5904452
## 46.42857  0.15837442 -0.4714635
## 46.57143 -0.24489365 -0.8787325
## 46.71429 -0.38174457 -1.0324362
## 46.85714 -0.50895771 -1.1741991
## 47.00000 -0.62546577 -1.3029649
## 47.14286 -0.73064198 -1.4182126
## 47.28571 -0.82422839 -1.5198583
## 47.42857 -0.90627002 -1.6081653
## 
## $山形県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 2.725172 3.254817
## 45.71429 2.687234 3.225134
## 45.85714 2.821243 3.371895
## 46.00000 2.345766 2.913069
## 46.14286 1.781089 2.367912
## 46.28571 2.314832 2.922873
## 46.42857 2.537959 3.167797
## 46.57143 2.149807 2.783645
## 46.71429 2.076627 2.727318
## 46.85714 2.004384 2.669626
## 47.00000 1.934187 2.611686
## 47.14286 1.867062 2.554632
## 47.28571 1.803924 2.499554
## 47.42857 1.745553 2.447449
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 6.447855 6.447855 6.447855 6.447855 6.447855 6.447855 6.447855 6.447855
##  [9] 6.447855 6.447855 6.447855 6.447855 6.447855 6.447855
## 
## $福島県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 3.834970 2.451792
## 45.71429 3.756746 2.332158
## 45.85714 3.680732 2.215905
## 46.00000 3.606751 2.102761
## 46.14286 3.534648 1.992490
## 46.28571 3.464288 1.884882
## 46.42857 3.395548 1.779755
## 46.57143 3.328323 1.676943
## 46.71429 3.262517 1.576301
## 46.85714 3.198043 1.477696
## 47.00000 3.134823 1.381010
## 47.14286 3.072787 1.286134
## 47.28571 3.011871 1.192971
## 47.42857 2.952017 1.101432
## 
## $福島県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 9.060741 10.44392
## 45.71429 9.138965 10.56355
## 45.85714 9.214979 10.67981
## 46.00000 9.288960 10.79295
## 46.14286 9.361063 10.90322
## 46.28571 9.431423 11.01083
## 46.42857 9.500163 11.11596
## 46.57143 9.567388 11.21877
## 46.71429 9.633194 11.31941
## 46.85714 9.697668 11.41802
## 47.00000 9.760888 11.51470
## 47.14286 9.822924 11.60958
## 47.28571 9.883840 11.70274
## 47.42857 9.943694 11.79428
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 41.50636 32.78905 56.96146 54.31464 44.41028 48.50268 60.73477 51.40053
##  [9] 46.59783 56.48688 55.47421 51.09463 52.32344 56.77970
## 
## $茨城県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 36.53896 33.90938
## 45.71429 27.57434 24.81384
## 45.85714 51.70587 48.92372
## 46.00000 48.68309 45.70192
## 46.14286 38.16651 34.86125
## 46.28571 42.05155 38.63652
## 46.42857 54.10902 50.60156
## 46.57143 43.78672 39.75621
## 46.71429 38.54712 34.28533
## 46.85714 48.20021 43.81351
## 47.00000 46.82292 42.24320
## 47.14286 41.97344 37.14498
## 47.28571 42.87440 37.87237
## 47.42857 47.04581 41.89301
## 
## $茨城県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 46.47375 49.10333
## 45.71429 38.00376 40.76426
## 45.85714 62.21706 64.99920
## 46.00000 59.94620 62.92737
## 46.14286 50.65405 53.95930
## 46.28571 54.95381 58.36884
## 46.42857 67.36052 70.86798
## 46.57143 59.01433 63.04484
## 46.71429 54.64853 58.91032
## 46.85714 64.77354 69.16024
## 47.00000 64.12551 68.70522
## 47.14286 60.21582 65.04428
## 47.28571 61.77249 66.77451
## 47.42857 66.51358 71.66638
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 5.529570 4.494222 5.461142 5.088439 7.669703 6.038288 5.929066 5.385718
##  [9] 5.262245 5.250971 5.250971 5.250971 5.250971 5.250971
## 
## $栃木県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%         95%
## 45.57143 2.494024  0.88710268
## 45.71429 1.392003 -0.25021200
## 45.85714 2.176551  0.43779318
## 46.00000 1.778640  0.02653791
## 46.14286 4.334886  2.56954090
## 46.28571 2.678640  0.90015017
## 46.42857 2.544769  0.75323086
## 46.57143 1.820872 -0.06624274
## 46.71429 1.654016 -0.25606450
## 46.85714 1.582697 -0.35917028
## 47.00000 1.548317 -0.41174973
## 47.14286 1.514253 -0.46384544
## 47.28571 1.480497 -0.51547053
## 47.42857 1.447041 -0.56663751
## 
## $栃木県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143  8.565116 10.172037
## 45.71429  7.596441  9.238656
## 45.85714  8.745733 10.484490
## 46.00000  8.398238 10.150339
## 46.14286 11.004519 12.769864
## 46.28571  9.397935 11.176426
## 46.42857  9.313363 11.104901
## 46.57143  8.950563 10.837678
## 46.71429  8.870473 10.780554
## 46.85714  8.919244 10.861111
## 47.00000  8.953624 10.913691
## 47.14286  8.987688 10.965786
## 47.28571  9.021444 11.017412
## 47.42857  9.054900 11.068579
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 14.178136 14.124442 13.212436 12.895246 12.826339 12.699605 11.751707
##  [8] 11.071383 10.610193 10.381878  9.986981  9.753991  9.509014  9.387020
## 
## $群馬県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%        95%
## 45.57143 9.562459  7.1190663
## 45.71429 8.770081  5.9356539
## 45.85714 7.612542  4.6481370
## 46.00000 7.209549  4.1997232
## 46.14286 6.819325  3.6394043
## 46.28571 6.584678  3.3476309
## 46.42857 5.511546  2.2082041
## 46.57143 4.709039  1.3410172
## 46.71429 4.099181  0.6524595
## 46.85714 3.813272  0.3360616
## 47.00000 3.345891 -0.1696896
## 47.14286 3.066192 -0.4741150
## 47.28571 2.762797 -0.8084345
## 47.42857 2.607471 -0.9814057
## 
## $群馬県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 18.79381 21.23721
## 45.71429 19.47880 22.31323
## 45.85714 18.81233 21.77674
## 46.00000 18.58094 21.59077
## 46.14286 18.83335 22.01327
## 46.28571 18.81453 22.05158
## 46.42857 17.99187 21.29521
## 46.57143 17.43373 20.80175
## 46.71429 17.12121 20.56793
## 46.85714 16.95048 20.42769
## 47.00000 16.62807 20.14365
## 47.14286 16.44179 19.98210
## 47.28571 16.25523 19.82646
## 47.42857 16.16657 19.75545
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 118.7610 123.4003 124.1466 142.0026 132.8528 119.2071 156.8608 135.7246
##  [9] 137.4147 135.7900 140.0593 139.8091 135.6815 151.5691
## 
## $埼玉県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 104.1310  96.38640
## 45.71429 108.0977  99.99702
## 45.85714 108.1998  99.75804
## 46.00000 125.4365 116.66694
## 46.14286 115.6898 106.60429
## 46.28571 101.4672  92.07634
## 46.42857 138.5623 128.87561
## 46.57143 114.6312 103.46505
## 46.71429 115.3923 103.73431
## 46.85714 112.8762 100.74634
## 47.00000 116.2875 103.70351
## 47.14286 115.2092 102.18681
## 47.28571 110.2806  96.83409
## 47.42857 125.3915 111.53396
## 
## $埼玉県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 133.3909 141.1355
## 45.71429 138.7028 146.8035
## 45.85714 140.0935 148.5352
## 46.00000 158.5687 167.3382
## 46.14286 150.0158 159.1014
## 46.28571 136.9469 146.3378
## 46.42857 175.1593 184.8459
## 46.57143 156.8180 167.9842
## 46.71429 159.4371 171.0951
## 46.85714 158.7038 170.8336
## 47.00000 163.8311 176.4151
## 47.14286 164.4089 177.4313
## 47.28571 161.0825 174.5290
## 47.42857 177.7467 191.6042
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1]  92.53409  93.81272  94.86077  94.02204 103.04267  98.80608 105.05119
##  [8]  99.16112 101.92228 101.46930  98.73237 104.46377 102.59755 104.29724
## 
## $千葉県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 80.58346 74.25719
## 45.71429 80.56433 73.55106
## 45.85714 80.97646 73.62655
## 46.00000 79.52967 71.85788
## 46.14286 87.96677 79.98606
## 46.28571 83.16839 74.89029
## 46.42857 88.87121 80.30605
## 46.57143 81.46163 72.09209
## 46.71429 83.35817 73.53092
## 46.85714 82.18960 71.98354
## 47.00000 78.76269 68.19138
## 47.14286 83.82717 72.90282
## 47.28571 81.31493 70.04860
## 47.42857 82.38764 70.78939
## 
## $千葉県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 104.4847 110.8110
## 45.71429 107.0611 114.0744
## 45.85714 108.7451 116.0950
## 46.00000 108.5144 116.1862
## 46.14286 118.1186 126.0993
## 46.28571 114.4438 122.7219
## 46.42857 121.2312 129.7963
## 46.57143 116.8606 126.2301
## 46.71429 120.4864 130.3136
## 46.85714 120.7490 130.9551
## 47.00000 118.7020 129.2734
## 47.14286 125.1004 136.0247
## 47.28571 123.8802 135.1465
## 47.42857 126.2069 137.8051
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 412.3836 351.5293 458.0252 518.2255 584.6704 560.7647 571.8777 459.7360
##  [9] 401.6670 507.1755 566.4080 631.9041 607.0683 617.2694
## 
## $東京都$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 364.4922 339.1401
## 45.71429 295.6845 266.1220
## 45.85714 398.5163 367.0142
## 46.00000 455.3965 422.1368
## 46.14286 518.8083 483.9429
## 46.28571 492.1139 455.7723
## 46.42857 500.6497 462.9438
## 46.57143 379.9082 337.6500
## 46.71429 316.8368 271.9304
## 46.85714 418.6332 371.7618
## 47.00000 474.4394 425.7541
## 47.14286 536.7589 486.3922
## 47.28571 508.9674 457.0359
## 47.42857 516.4096 463.0176
## 
## $東京都$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 460.2749 485.6270
## 45.71429 407.3741 436.9366
## 45.85714 517.5341 549.0361
## 46.00000 581.0545 614.3142
## 46.14286 650.5326 685.3979
## 46.28571 629.4156 665.7572
## 46.42857 643.1057 680.8115
## 46.57143 539.5637 581.8220
## 46.71429 486.4971 531.4035
## 46.85714 595.7178 642.5893
## 47.00000 658.3766 707.0618
## 47.14286 727.0492 777.4160
## 47.28571 705.1692 757.1006
## 47.42857 718.1292 771.5212
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 160.0242 136.7828 189.7533 238.2221 228.2744 224.0306 211.5576 194.5097
##  [9] 186.9794 215.6176 240.3739 233.9144 230.0333 223.1471
## 
## $神奈川県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 139.7340 128.9931
## 45.71429 113.7440 101.5479
## 45.85714 165.4120 152.5265
## 46.00000 213.5888 200.5487
## 46.14286 203.2884 190.0616
## 46.28571 198.5349 185.0382
## 46.42857 184.3299 169.9164
## 46.57143 161.2546 143.6504
## 46.71429 151.0873 132.0871
## 46.85714 178.2891 158.5285
## 47.00000 202.2159 182.0162
## 47.14286 194.8415 174.1576
## 47.28571 189.6950 168.3412
## 47.42857 180.9060 158.5449
## 
## $神奈川県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 180.3144 191.0553
## 45.71429 159.8216 172.0176
## 45.85714 214.0947 226.9802
## 46.00000 262.8554 275.8955
## 46.14286 253.2604 266.4872
## 46.28571 249.5263 263.0230
## 46.42857 238.7854 253.1988
## 46.57143 227.7647 245.3689
## 46.71429 222.8716 241.8717
## 46.85714 252.9462 272.7067
## 47.00000 278.5319 298.7315
## 47.14286 272.9873 293.6713
## 47.28571 270.3716 291.7255
## 47.42857 265.3882 287.7493
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 9.293920 9.336197 9.995707 9.975089 9.814519 9.822057 9.861112 9.858662
##  [9] 9.849173 9.849919 9.852222 9.852004 9.851446 9.851508
## 
## $新潟県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 6.370940 4.823608
## 45.71429 6.326899 4.733873
## 45.85714 6.986182 5.393036
## 46.00000 6.909025 5.285948
## 46.14286 6.659696 4.989634
## 46.28571 6.609435 4.908776
## 46.42857 6.598229 4.870964
## 46.57143 6.540286 4.783643
## 46.71429 6.474719 4.688390
## 46.85714 6.421792 4.607051
## 47.00000 6.371578 4.529036
## 47.14286 6.319269 4.449152
## 47.28571 6.267304 4.369974
## 47.42857 6.216772 4.292659
## 
## $新潟県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 12.21690 13.76423
## 45.71429 12.34549 13.93852
## 45.85714 13.00523 14.59838
## 46.00000 13.04115 14.66423
## 46.14286 12.96934 14.63940
## 46.28571 13.03468 14.73534
## 46.42857 13.12399 14.85126
## 46.57143 13.17704 14.93368
## 46.71429 13.22363 15.00996
## 46.85714 13.27805 15.09279
## 47.00000 13.33287 15.17541
## 47.14286 13.38474 15.25486
## 47.28571 13.43559 15.33292
## 47.42857 13.48624 15.41036
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 2.475621 2.112902 2.147680 1.872117 2.011218 1.928063 1.911810 1.851604
##  [9] 1.843127 1.808718 1.785760 1.757880 1.737611 1.715286
## 
## $富山県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                  80%       95%
## 45.57143  0.02613957 -1.270537
## 45.71429 -0.46984778 -1.837073
## 45.85714 -0.58359354 -2.029443
## 46.00000 -0.96422813 -2.465699
## 46.14286 -0.99630564 -2.588393
## 46.28571 -1.17559801 -2.818577
## 46.42857 -1.28036080 -2.970194
## 46.57143 -1.41312801 -3.141373
## 46.71429 -1.49073243 -3.255571
## 46.85714 -1.58107618 -3.375525
## 47.00000 -1.65329876 -3.473826
## 47.14286 -1.72352769 -3.566473
## 47.28571 -1.78121916 -3.643975
## 47.42857 -1.83582061 -3.715663
## 
## $富山県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 4.925102 6.221779
## 45.71429 4.695653 6.062878
## 45.85714 4.878954 6.324803
## 46.00000 4.708463 6.209934
## 46.14286 5.018742 6.610829
## 46.28571 5.031724 6.674703
## 46.42857 5.103980 6.793814
## 46.57143 5.116335 6.844580
## 46.71429 5.176987 6.941825
## 46.85714 5.198512 6.992961
## 47.00000 5.224819 7.045346
## 47.14286 5.239287 7.082232
## 47.28571 5.256441 7.119198
## 47.42857 5.266392 7.146235
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801
##  [8] 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801 0.9397801
## 
## $石川県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 -2.705794 -4.635644
## 45.71429 -2.910323 -4.948444
## 45.85714 -3.104522 -5.245446
## 46.00000 -3.289814 -5.528826
## 46.14286 -3.467322 -5.800302
## 46.28571 -3.637953 -6.061258
## 46.42857 -3.802448 -6.312832
## 46.57143 -3.961425 -6.555966
## 46.71429 -4.115405 -6.791459
## 46.85714 -4.264832 -7.019987
## 47.00000 -4.410086 -7.242135
## 47.14286 -4.551500 -7.458408
## 47.28571 -4.689362 -7.669250
## 47.42857 -4.823928 -7.875051
## 
## $石川県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 4.585354 6.515204
## 45.71429 4.789883 6.828005
## 45.85714 4.984082 7.125006
## 46.00000 5.169374 7.408386
## 46.14286 5.346883 7.679862
## 46.28571 5.517513 7.940818
## 46.42857 5.682008 8.192392
## 46.57143 5.840985 8.435526
## 46.71429 5.994965 8.671019
## 46.85714 6.144392 8.899547
## 47.00000 6.289647 9.121695
## 47.14286 6.431060 9.337968
## 47.28571 6.568922 9.548810
## 47.42857 6.703488 9.754611
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 2.420876 1.783501 1.665337 1.564296 1.477896 1.404017 1.340842 1.286822
##  [9] 1.240630 1.201131 1.167356 1.138475 1.113779 1.092662
## 
## $福井県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%        95%
## 45.57143  0.4039530 -0.6637417
## 45.71429 -0.3702189 -1.5103291
## 45.85714 -0.7050827 -1.9599071
## 46.00000 -0.9527921 -2.2852581
## 46.14286 -1.1412406 -2.5277281
## 46.28571 -1.2872894 -2.7119808
## 46.42857 -1.4020311 -2.8540206
## 46.57143 -1.4931512 -2.9647803
## 46.71429 -1.5661601 -3.0519852
## 46.85714 -1.6251059 -3.1212255
## 47.00000 -1.6730161 -3.1766185
## 47.14286 -1.7121882 -3.2212383
## 47.28571 -1.7443853 -3.2574064
## 47.42857 -1.7709751 -3.2868930
## 
## $福井県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 4.437800 5.505495
## 45.71429 3.937220 5.077330
## 45.85714 4.035757 5.290581
## 46.00000 4.081384 5.413850
## 46.14286 4.097034 5.483521
## 46.28571 4.095323 5.520014
## 46.42857 4.083716 5.535705
## 46.57143 4.066796 5.538425
## 46.71429 4.047420 5.533246
## 46.85714 4.027369 5.523488
## 47.00000 4.007729 5.511331
## 47.14286 3.989139 5.498189
## 47.28571 3.971944 5.484965
## 47.42857 3.956299 5.472217
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 3.858203 4.132650 4.220417 4.248484 4.257460 4.260330 4.261248 4.261542
##  [9] 4.261636 4.261666 4.261675 4.261678 4.261679 4.261680
## 
## $山梨県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 2.039756 1.0771288
## 45.71429 2.125059 1.0623046
## 45.85714 2.141155 1.0404606
## 46.00000 2.122713 0.9973974
## 46.14286 2.092399 0.9462856
## 46.28571 2.058544 0.8929895
## 46.42857 2.023917 0.8395449
## 46.57143 1.989401 0.7866026
## 46.71429 1.955268 0.7343510
## 46.85714 1.921590 0.6828283
## 47.00000 1.888375 0.6320261
## 47.14286 1.855615 0.5819216
## 47.28571 1.823293 0.5324891
## 47.42857 1.791393 0.4837029
## 
## $山梨県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 5.676649 6.639276
## 45.71429 6.140241 7.202996
## 45.85714 6.299679 7.400373
## 46.00000 6.374256 7.499571
## 46.14286 6.422520 7.568634
## 46.28571 6.462116 7.627671
## 46.42857 6.498580 7.682951
## 46.57143 6.533682 7.736481
## 46.71429 6.568003 7.788920
## 46.85714 6.601741 7.840503
## 47.00000 6.634975 7.891324
## 47.14286 6.667742 7.941435
## 47.28571 6.700066 7.990869
## 47.42857 6.731966 8.039656
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 26.10529 26.11555 26.05657 26.68827 26.89646 26.89646 26.89646 26.89646
##  [9] 26.89646 26.89646 26.89646 26.89646 26.89646 26.89646
## 
## $長野県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 23.01715 21.38238
## 45.71429 22.89436 21.18916
## 45.85714 22.70841 20.93600
## 46.00000 23.20707 21.36423
## 46.14286 23.02984 20.98297
## 46.28571 22.56148 20.26668
## 46.42857 22.13901 19.62056
## 46.57143 21.75111 19.02732
## 46.71429 21.39047 18.47577
## 46.85714 21.05204 17.95819
## 47.00000 20.73217 17.46899
## 47.14286 20.42809 17.00394
## 47.28571 20.13768 16.55980
## 47.42857 19.85925 16.13397
## 
## $長野県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 29.19344 30.82820
## 45.71429 29.33675 31.04194
## 45.85714 29.40473 31.17714
## 46.00000 30.16947 32.01231
## 46.14286 30.76308 32.80995
## 46.28571 31.23144 33.52624
## 46.42857 31.65391 34.17236
## 46.57143 32.04181 34.76560
## 46.71429 32.40245 35.31715
## 46.85714 32.74088 35.83473
## 47.00000 33.06075 36.32393
## 47.14286 33.36483 36.78898
## 47.28571 33.65524 37.23312
## 47.42857 33.93367 37.65895
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 18.75945 19.10965 18.37347 19.79605 19.58301 18.98103 20.53429 19.81854
##  [9] 19.64110 19.59711 19.58620 19.58350 19.58283 19.58266
## 
## $岐阜県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 15.28102 13.439654
## 45.71429 15.14086 13.039910
## 45.85714 14.12271 11.872489
## 46.00000 15.31245 12.938981
## 46.14286 14.88527 12.398439
## 46.28571 14.08019 11.485835
## 46.42857 15.43883 12.741459
## 46.57143 14.27692 11.343357
## 46.71429 13.80667 10.718109
## 46.85714 13.50947 10.286863
## 47.00000 13.26139  9.913241
## 47.14286 13.03151  9.563093
## 47.28571 12.81161  9.227143
## 47.42857 12.59918  8.902340
## 
## $岐阜県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 22.23788 24.07925
## 45.71429 23.07844 25.17939
## 45.85714 22.62423 24.87445
## 46.00000 24.27964 26.65312
## 46.14286 24.28075 26.76758
## 46.28571 23.88188 26.47623
## 46.42857 25.62976 28.32713
## 46.57143 25.36016 28.29372
## 46.71429 25.47553 28.56409
## 46.85714 25.68475 28.90736
## 47.00000 25.91101 29.25917
## 47.14286 26.13549 29.60391
## 47.28571 26.35405 29.93852
## 47.42857 26.56615 30.26299
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 81.44613 70.15131 55.83282 81.12729 85.70459 71.73309 67.56817 84.59662
##  [9] 82.39362 68.74647 74.71402 84.79597 76.64325 70.29969
## 
## $静岡県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 75.32528 72.08510
## 45.71429 62.88989 59.04593
## 45.85714 47.85932 43.63840
## 46.00000 72.70221 68.24225
## 46.14286 75.99585 70.85636
## 46.28571 60.94614 55.23587
## 46.42857 56.39504 50.48034
## 46.57143 72.49584 66.09007
## 46.71429 69.05407 61.99254
## 46.85714 54.65233 47.19135
## 47.00000 60.18112 52.48787
## 47.14286 69.42183 61.28325
## 47.28571 60.33020 51.69460
## 47.42857 53.49193 44.59444
## 
## $静岡県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143  87.56697  90.80715
## 45.71429  77.41273  81.25669
## 45.85714  63.80632  68.02723
## 46.00000  89.55236  94.01233
## 46.14286  95.41334 100.55283
## 46.28571  82.52003  88.23030
## 46.42857  78.74130  84.65599
## 46.57143  96.69741 103.10318
## 46.71429  95.73316 102.79469
## 46.85714  82.84061  90.30159
## 47.00000  89.24691  96.94016
## 47.14286 100.17011 108.30869
## 47.28571  92.95630 101.59190
## 47.42857  87.10745  96.00495
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 181.1631 160.3586 201.5328 203.2679 246.3125 236.9338 241.9007 225.4262
##  [9] 213.9380 236.6738 237.6319 261.4004 256.2217 258.9643
## 
## $愛知県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 164.9150 156.3137
## 45.71429 140.7829 130.4201
## 45.85714 178.2164 165.8734
## 46.00000 177.0020 163.0977
## 46.14286 217.3172 201.9680
## 46.28571 205.4689 188.8123
## 46.42857 208.1390 190.2665
## 46.57143 185.5574 164.4522
## 46.71429 170.0848 146.8704
## 46.85714 188.7901 163.4420
## 47.00000 186.1486 158.8950
## 47.14286 206.5174 177.4640
## 47.28571 198.1487 167.4067
## 47.42857 197.8642 165.5197
## 
## $愛知県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 197.4112 206.0124
## 45.71429 179.9344 190.2972
## 45.85714 224.8492 237.1921
## 46.00000 229.5339 243.4382
## 46.14286 275.3078 290.6570
## 46.28571 268.3988 285.0554
## 46.42857 275.6625 293.5349
## 46.57143 265.2949 286.4001
## 46.71429 257.7911 281.0055
## 46.85714 284.5575 309.9056
## 47.00000 289.1152 316.3688
## 47.14286 316.2835 345.3368
## 47.28571 314.2947 345.0367
## 47.42857 320.0644 352.4089
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 18.88363 18.58504 19.17326 20.15593 20.56131 20.26208 20.63810 20.33996
##  [9] 20.33996 20.33996 20.33996 20.33996 20.33996 20.33996
## 
## $三重県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 14.97556 12.906761
## 45.71429 14.30355 12.037068
## 45.85714 14.54840 12.100152
## 46.00000 15.21149 12.594067
## 46.14286 15.31673 12.540417
## 46.28571 14.73363 11.807041
## 46.42857 14.83965 11.770140
## 46.57143 14.15955 10.887837
## 46.71429 13.86717 10.440688
## 46.85714 13.58745 10.012882
## 47.00000 13.31885  9.602107
## 47.14286 13.06017  9.206477
## 47.28571 12.81036  8.824431
## 47.42857 12.56858  8.454660
## 
## $三重県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 22.79169 24.86049
## 45.71429 22.86652 25.13300
## 45.85714 23.79811 26.24637
## 46.00000 25.10037 27.71780
## 46.14286 25.80589 28.58221
## 46.28571 25.79054 28.71712
## 46.42857 26.43654 29.50606
## 46.57143 26.52037 29.79208
## 46.71429 26.81274 30.23923
## 46.85714 27.09247 30.66703
## 47.00000 27.36106 31.07781
## 47.14286 27.61975 31.47344
## 47.28571 27.86956 31.85549
## 47.42857 28.11134 32.22526
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 11.34025 10.74917 11.29344 11.16156 11.40419 11.40004 11.51069 11.52484
##  [9] 11.56867 11.57195 11.58016 11.57115 11.56215 11.54689
## 
## $滋賀県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 7.479928 5.436398
## 45.71429 6.816468 4.734621
## 45.85714 6.927112 4.615717
## 46.00000 6.735538 4.392544
## 46.14286 6.850320 4.439649
## 46.28571 6.797187 4.360582
## 46.42857 6.834839 4.359590
## 46.57143 6.790580 4.284416
## 46.71429 6.756605 4.209254
## 46.85714 6.675459 4.083410
## 47.00000 6.579402 3.932158
## 47.14286 6.452302 3.742548
## 47.28571 6.308150 3.526850
## 47.42857 6.145546 3.286249
## 
## $滋賀県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 15.20057 17.24410
## 45.71429 14.68187 16.76372
## 45.85714 15.65977 17.97116
## 46.00000 15.58758 17.93057
## 46.14286 15.95805 18.36872
## 46.28571 16.00290 18.43950
## 46.42857 16.18655 18.66180
## 46.57143 16.25910 18.76526
## 46.71429 16.38073 18.92808
## 46.85714 16.46845 19.06050
## 47.00000 16.58093 19.22817
## 47.14286 16.69000 19.39975
## 47.28571 16.81615 19.59745
## 47.42857 16.94823 19.80752
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 30.71216 29.67974 29.93373 30.61335 31.35937 32.03578 32.60290 33.06031
##  [9] 33.42155 33.70346 33.92194 34.09056 34.22039 34.32020
## 
## $京都府$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 23.76493 20.08729
## 45.71429 22.12310 18.12286
## 45.85714 22.22547 18.14496
## 46.00000 22.82007 18.69456
## 46.14286 23.47354 19.29903
## 46.28571 24.02539 19.78494
## 46.42857 24.42714 20.09915
## 46.57143 24.67875 20.24182
## 46.71429 24.79938 20.23508
## 46.85714 24.81348 20.10741
## 47.00000 24.74456 19.88636
## 47.14286 24.61286 19.59567
## 47.28571 24.43478 19.25459
## 47.42857 24.22318 18.87815
## 
## $京都府$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 37.65940 41.33704
## 45.71429 37.23638 41.23662
## 45.85714 37.64200 41.72251
## 46.00000 38.40664 42.53215
## 46.14286 39.24521 43.41972
## 46.28571 40.04617 44.28661
## 46.42857 40.77867 45.10666
## 46.57143 41.44187 45.87880
## 46.71429 42.04372 46.60803
## 46.85714 42.59344 47.29952
## 47.00000 43.09931 47.95752
## 47.14286 43.56826 48.58545
## 47.28571 44.00600 49.18618
## 47.42857 44.41721 49.76225
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 362.2006 218.8209 356.8911 359.7089 405.4975 428.0395 459.7393 422.5453
##  [9] 321.5429 418.8051 420.7901 453.0454 468.9249 491.2555
## 
## $大阪府$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 333.6379 318.5177
## 45.71429 187.2868 170.5937
## 45.85714 323.7999 306.2824
## 46.00000 325.1305 306.8258
## 46.14286 369.4933 350.4338
## 46.28571 390.6639 370.8785
## 46.42857 421.0409 400.5552
## 46.57143 373.4868 347.5168
## 46.71429 268.7713 240.8357
## 46.85714 363.3326 333.9672
## 47.00000 362.7422 332.0135
## 47.14286 392.5316 360.4976
## 47.28571 406.0420 372.7537
## 47.42857 426.0894 391.5926
## 
## $大阪府$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 390.7633 405.8834
## 45.71429 250.3549 267.0481
## 45.85714 389.9824 407.4998
## 46.00000 394.2873 412.5920
## 46.14286 441.5016 460.5611
## 46.28571 465.4151 485.2006
## 46.42857 498.4378 518.9235
## 46.57143 471.6038 497.5738
## 46.71429 374.3146 402.2502
## 46.85714 474.2776 503.6430
## 47.00000 478.8379 509.5666
## 47.14286 513.5591 545.5931
## 47.28571 531.8079 565.0961
## 47.42857 556.4216 590.9185
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 137.5975 118.0047 156.3897 155.3103 172.1419 170.4254 182.9428 174.6384
##  [9] 159.4931 188.5956 187.7930 200.3085 199.0322 208.3397
## 
## $兵庫県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 127.3925 121.9904
## 45.71429 107.0052 101.1824
## 45.85714 143.9235 137.3244
## 46.00000 141.9357 134.8556
## 46.14286 157.9167 150.3863
## 46.28571 155.3976 147.4424
## 46.42857 167.1532 158.7947
## 46.57143 155.7877 145.8087
## 46.71429 139.3713 128.7195
## 46.85714 166.9493 155.4905
## 47.00000 164.9058 152.7901
## 47.14286 176.2443 163.5055
## 47.28571 173.8459 160.5130
## 47.42857 182.0792 168.1777
## 
## $兵庫県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 147.8025 153.2046
## 45.71429 129.0042 134.8269
## 45.85714 168.8558 175.4550
## 46.00000 168.6850 175.7651
## 46.14286 186.3672 193.8976
## 46.28571 185.4532 193.4085
## 46.42857 198.7323 207.0908
## 46.57143 193.4891 203.4681
## 46.71429 179.6148 190.2667
## 46.85714 210.2418 221.7007
## 47.00000 210.6802 222.7960
## 47.14286 224.3728 237.1116
## 47.28571 224.2185 237.5514
## 47.42857 234.6003 248.5017
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 21.16281 21.40839 21.18658 23.15169 23.60179 22.54347 22.43791 22.50997
##  [9] 22.50997 22.50997 22.50997 22.50997 22.50997 22.50997
## 
## $奈良県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 16.91049 14.65945
## 45.71429 16.95291 14.59432
## 45.85714 16.53681 14.07536
## 46.00000 18.31542 15.75525
## 46.14286 18.58596 15.93074
## 46.28571 17.35429 14.60730
## 46.42857 17.08098 14.24519
## 46.57143 16.86084 13.87037
## 46.71429 16.67146 13.58075
## 46.85714 16.48804 13.30022
## 47.00000 16.31004 13.02799
## 47.14286 16.13701 12.76336
## 47.28571 15.96855 12.50573
## 47.42857 15.80432 12.25457
## 
## $奈良県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 25.41514 27.66618
## 45.71429 25.86388 28.22247
## 45.85714 25.83636 28.29780
## 46.00000 27.98796 30.54812
## 46.14286 28.61762 31.27285
## 46.28571 27.73266 30.47965
## 46.42857 27.79484 30.63063
## 46.57143 28.15910 31.14957
## 46.71429 28.34848 31.43920
## 46.85714 28.53190 31.71972
## 47.00000 28.70991 31.99195
## 47.14286 28.88294 32.25658
## 47.28571 29.05139 32.51421
## 47.42857 29.21562 32.76537
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 8.918729 8.963291 9.278489 8.759375 7.957174 8.442803 8.876234 8.642471
##  [9] 8.682555 8.648635 8.677339 8.653049 8.673604 8.656210
## 
## $和歌山県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 6.890901 5.817434
## 45.71429 6.730351 5.548303
## 45.85714 6.967311 5.743847
## 46.00000 6.285341 4.975666
## 46.14286 5.401622 4.048794
## 46.28571 5.751016 4.326071
## 46.42857 6.101867 4.633205
## 46.57143 5.810900 4.311957
## 46.71429 5.792583 4.262724
## 46.85714 5.669007 4.091686
## 47.00000 5.637760 4.028704
## 47.14286 5.532499 3.880579
## 47.28571 5.492653 3.808759
## 47.42857 5.400748 3.677410
## 
## $和歌山県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 10.94656 12.02002
## 45.71429 11.19623 12.37828
## 45.85714 11.58967 12.81313
## 46.00000 11.23341 12.54308
## 46.14286 10.51273 11.86555
## 46.28571 11.13459 12.55953
## 46.42857 11.65060 13.11926
## 46.57143 11.47404 12.97299
## 46.71429 11.57253 13.10238
## 46.85714 11.62826 13.20558
## 47.00000 11.71692 13.32597
## 47.14286 11.77360 13.42552
## 47.28571 11.85455 13.53845
## 47.42857 11.91167 13.63501
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 0.1869554 0.2114582 0.2375735 0.2721550 0.2835770 0.2932437 0.3011182
##  [8] 0.3050548 0.3079913 0.3100444 0.3112661 0.3121200 0.3126908 0.3130543
## 
## $鳥取県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%       95%
## 45.57143 -0.7465862 -1.240773
## 45.71429 -0.7316947 -1.230970
## 45.85714 -0.7164972 -1.221552
## 46.00000 -0.7002363 -1.214989
## 46.14286 -0.6916588 -1.207917
## 46.28571 -0.6842496 -1.201703
## 46.42857 -0.6780982 -1.196464
## 46.57143 -0.6749340 -1.193709
## 46.71429 -0.6725927 -1.191682
## 46.85714 -0.6709964 -1.190328
## 47.00000 -0.6701168 -1.189630
## 47.14286 -0.6695601 -1.189230
## 47.28571 -0.6692539 -1.189064
## 47.42857 -0.6691323 -1.189070
## 
## $鳥取県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 1.120497 1.614684
## 45.71429 1.154611 1.653886
## 45.85714 1.191644 1.696699
## 46.00000 1.244546 1.759299
## 46.14286 1.258813 1.775071
## 46.28571 1.270737 1.788191
## 46.42857 1.280335 1.798700
## 46.57143 1.285044 1.803818
## 46.71429 1.288575 1.807665
## 46.85714 1.291085 1.810417
## 47.00000 1.292649 1.812162
## 47.14286 1.293800 1.813470
## 47.28571 1.294636 1.814446
## 47.42857 1.295241 1.815179
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282
##  [8] 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282 0.4551282
## 
## $島根県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 -6.250781 -9.800676
## 45.71429 -6.250781 -9.800676
## 45.85714 -6.250781 -9.800676
## 46.00000 -6.250781 -9.800676
## 46.14286 -6.250781 -9.800676
## 46.28571 -6.250781 -9.800676
## 46.42857 -6.250781 -9.800676
## 46.57143 -6.250781 -9.800676
## 46.71429 -6.250781 -9.800676
## 46.85714 -6.250781 -9.800676
## 47.00000 -6.250781 -9.800676
## 47.14286 -6.250781 -9.800676
## 47.28571 -6.250781 -9.800676
## 47.42857 -6.250781 -9.800676
## 
## $島根県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 7.161038 10.71093
## 45.71429 7.161038 10.71093
## 45.85714 7.161038 10.71093
## 46.00000 7.161038 10.71093
## 46.14286 7.161038 10.71093
## 46.28571 7.161038 10.71093
## 46.42857 7.161038 10.71093
## 46.57143 7.161038 10.71093
## 46.71429 7.161038 10.71093
## 46.85714 7.161038 10.71093
## 47.00000 7.161038 10.71093
## 47.14286 7.161038 10.71093
## 47.28571 7.161038 10.71093
## 47.42857 7.161038 10.71093
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 20.61769 16.00274 17.54658 19.78142 17.43543 16.61971 19.77731 17.69522
##  [9] 18.86966 18.19578 16.49423 17.64631 17.87584 16.22137
## 
## $岡山県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 17.48581 15.827893
## 45.71429 12.58042 10.768750
## 45.85714 14.00545 12.130892
## 46.00000 16.12534 14.189928
## 46.14286 13.66790 11.673496
## 46.28571 12.74395 10.692239
## 46.42857 15.79624 13.688795
## 46.57143 13.41195 11.144523
## 46.71429 14.42009 12.064637
## 46.85714 13.61121 11.184284
## 47.00000 11.77852  9.282174
## 47.14286 12.80300 10.239115
## 47.28571 12.90822 10.278526
## 47.42857 11.13247  8.438570
## 
## $岡山県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 23.74957 25.40749
## 45.71429 19.42506 21.23672
## 45.85714 21.08771 22.96227
## 46.00000 23.43750 25.37291
## 46.14286 21.20295 23.19736
## 46.28571 20.49548 22.54719
## 46.42857 23.75838 25.86583
## 46.57143 21.97849 24.24591
## 46.71429 23.31922 25.67467
## 46.85714 22.78034 25.20727
## 47.00000 21.20994 23.70629
## 47.14286 22.48961 25.05350
## 47.28571 22.84346 25.47316
## 47.42857 21.31027 24.00417
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 7.040722 6.388856 6.480883 6.799257 6.696913 6.551360 6.629060 6.690249
##  [9] 6.639802 6.617158 6.646953 6.653405 6.637007 6.636538
## 
## $広島県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                   80%        95%
## 45.57143  3.447404021  1.5452162
## 45.71429  2.455142480  0.3727605
## 45.85714  2.340701757  0.1490223
## 46.00000  2.030369860 -0.4941263
## 46.14286  1.385242989 -1.4265855
## 46.28571  0.945454201 -2.0221333
## 46.42857  0.709554245 -2.4240426
## 46.57143  0.388027514 -2.9481669
## 46.71429  0.008947961 -3.5012139
## 46.85714 -0.293059210 -3.9511074
## 47.00000 -0.551626383 -4.3623240
## 47.14286 -0.835332086 -4.7996298
## 47.28571 -1.119177336 -5.2250532
## 47.42857 -1.374044037 -5.6145901
## 
## $広島県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 10.63404 12.53623
## 45.71429 10.32257 12.40495
## 45.85714 10.62106 12.81274
## 46.00000 11.56814 14.09264
## 46.14286 12.00858 14.82041
## 46.28571 12.15727 15.12485
## 46.42857 12.54856 15.68216
## 46.57143 12.99247 16.32866
## 46.71429 13.27066 16.78082
## 46.85714 13.52738 17.18542
## 47.00000 13.84553 17.65623
## 47.14286 14.14214 18.10644
## 47.28571 14.39319 18.49907
## 47.42857 14.64712 18.88767
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 16.04385 14.74316 17.43122 15.93114 17.60936 20.61162 18.86641 18.54513
##  [9] 17.42135 18.96781 18.16609 19.95936 20.53758 19.65023
## 
## $山口県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 13.57341 12.26564
## 45.71429 12.09612 10.69486
## 45.85714 14.61864 13.12974
## 46.00000 12.96224 11.39059
## 46.14286 14.49196 12.84171
## 46.28571 17.35249 15.62720
## 46.42857 15.47146 13.67428
## 46.57143 14.86921 12.92330
## 46.71429 13.57649 11.54114
## 46.85714 14.96112 12.84010
## 47.00000 14.00385 11.80050
## 47.14286 15.64718 13.36445
## 47.28571 16.08050 13.72107
## 47.42857 15.05283 12.61911
## 
## $山口県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 18.51429 19.82206
## 45.71429 17.39021 18.79147
## 45.85714 20.24380 21.73269
## 46.00000 18.90005 20.47169
## 46.14286 20.72676 22.37701
## 46.28571 23.87075 25.59604
## 46.42857 22.26137 24.05855
## 46.57143 22.22104 24.16695
## 46.71429 21.26621 23.30156
## 46.85714 22.97451 25.09552
## 47.00000 22.32833 24.53169
## 47.14286 24.27153 26.55426
## 47.28571 24.99465 27.35409
## 47.42857 24.24764 26.68136
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 1.0132244 0.7234661 0.9737696 1.1038709 1.1059169 1.2838689 1.0245395
##  [8] 1.0894447 1.0898626 0.8208269 0.9187015 0.9181202 0.8451642 0.8310439
## 
## $徳島県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%       95%
## 45.57143 -0.7043745 -1.613616
## 45.71429 -1.0770265 -2.030150
## 45.85714 -0.8454039 -1.808416
## 46.00000 -0.7337938 -1.706595
## 46.14286 -0.7500546 -1.732547
## 46.28571 -0.5902306 -1.582319
## 46.42857 -0.8675145 -1.869107
## 46.57143 -0.8963052 -1.947498
## 46.71429 -0.9296611 -1.998732
## 46.85714 -1.2226440 -2.304392
## 47.00000 -1.1484391 -2.242717
## 47.14286 -1.1724221 -2.279088
## 47.28571 -1.2685208 -2.387438
## 47.42857 -1.3055332 -2.436569
## 
## $徳島県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 2.730823 3.640065
## 45.71429 2.523959 3.477082
## 45.85714 2.792943 3.755956
## 46.00000 2.941536 3.914336
## 46.14286 2.961888 3.944380
## 46.28571 3.157969 4.150057
## 46.42857 2.916593 3.918186
## 46.57143 3.075195 4.126387
## 46.71429 3.109386 4.178457
## 46.85714 2.864298 3.946046
## 47.00000 2.985842 4.080120
## 47.14286 3.008663 4.115329
## 47.28571 2.958849 4.077767
## 47.42857 2.967621 4.098657
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 1.736553 1.688877 1.688877 1.688877 1.688877 1.688877 1.688877 1.688877
##  [9] 1.688877 1.688877 1.688877 1.688877 1.688877 1.688877
## 
## $香川県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%        95%
## 45.57143 0.29806781 -0.4634200
## 45.71429 0.19147422 -0.6012030
## 45.85714 0.18168440 -0.6161752
## 46.00000 0.17195776 -0.6310509
## 46.14286 0.16229310 -0.6458317
## 46.28571 0.15268923 -0.6605195
## 46.42857 0.14314504 -0.6751161
## 46.57143 0.13365941 -0.6896231
## 46.71429 0.12423130 -0.7040422
## 46.85714 0.11485965 -0.7183749
## 47.00000 0.10554347 -0.7326228
## 47.14286 0.09628179 -0.7467873
## 47.28571 0.08707366 -0.7608699
## 47.42857 0.07791816 -0.7748720
## 
## $香川県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 3.175037 3.936525
## 45.71429 3.186280 3.978957
## 45.85714 3.196070 3.993930
## 46.00000 3.205797 4.008805
## 46.14286 3.215461 4.023586
## 46.28571 3.225065 4.038274
## 46.42857 3.234609 4.052870
## 46.57143 3.244095 4.067377
## 46.71429 3.253523 4.081797
## 46.85714 3.262895 4.096129
## 47.00000 3.272211 4.110377
## 47.14286 3.281473 4.124542
## 47.28571 3.290681 4.138624
## 47.42857 3.299836 4.152626
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 14.16957 14.72669 14.22334 13.90916 13.57413 13.25319 12.94020 12.63590
##  [9] 12.33989 12.05198 11.77193 11.49954 11.23459 10.97688
## 
## $愛媛県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 12.107789 11.016349
## 45.71429 12.486400 11.300462
## 45.85714 11.752629 10.444715
## 46.00000 11.250600  9.843244
## 46.14286 10.747513  9.251195
## 46.28571 10.276585  8.700866
## 46.42857  9.828290  8.180946
## 46.57143  9.401195  7.688848
## 46.71429  8.993157  7.221504
## 46.85714  8.602601  6.776612
## 47.00000  8.228182  6.352235
## 47.14286  7.868761  5.946745
## 47.28571  7.523356  5.558747
## 47.42857  7.191102  5.187030
## 
## $愛媛県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 16.23135 17.32279
## 45.71429 16.96698 18.15292
## 45.85714 16.69404 18.00196
## 46.00000 16.56771 17.97507
## 46.14286 16.40074 17.89706
## 46.28571 16.22980 17.80552
## 46.42857 16.05210 17.69945
## 46.57143 15.87060 17.58295
## 46.71429 15.68662 17.45828
## 46.85714 15.50135 17.32734
## 47.00000 15.31568 17.19163
## 47.14286 15.13031 17.05233
## 47.28571 14.94582 16.91043
## 47.42857 14.76266 16.76674
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 0.4690073 0.4679556 0.4669849 0.4660888 0.4652616 0.4644981 0.4637933
##  [8] 0.4631427 0.4625421 0.4619877 0.4614760 0.4610036 0.4605676 0.4601650
## 
## $高知県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 -1.214867 -2.106257
## 45.71429 -1.237440 -2.140222
## 45.85714 -1.256537 -2.168914
## 46.00000 -1.272729 -2.193204
## 46.14286 -1.286484 -2.213802
## 46.28571 -1.298189 -2.231299
## 46.42857 -1.308163 -2.246180
## 46.57143 -1.316674 -2.258852
## 46.71429 -1.323945 -2.269654
## 46.85714 -1.330163 -2.278871
## 47.00000 -1.335488 -2.286743
## 47.14286 -1.340050 -2.293471
## 47.28571 -1.343964 -2.299226
## 47.42857 -1.347325 -2.304153
## 
## $高知県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 2.152882 3.044271
## 45.71429 2.173352 3.076134
## 45.85714 2.190507 3.102884
## 46.00000 2.204907 3.125381
## 46.14286 2.217007 3.144326
## 46.28571 2.227185 3.160295
## 46.42857 2.235750 3.173767
## 46.57143 2.242959 3.185137
## 46.71429 2.249029 3.194738
## 46.85714 2.254139 3.202846
## 47.00000 2.258439 3.209695
## 47.14286 2.262057 3.215478
## 47.28571 2.265099 3.220361
## 47.42857 2.267655 3.224483
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 31.44385 28.08858 30.00726 33.56471 32.69618 37.34639 36.09025 35.02368
##  [9] 33.48827 35.32925 37.83373 37.22228 40.49608 39.61174
## 
## $福岡県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                80%       95%
## 45.57143 17.677089 10.389405
## 45.71429 11.813404  3.197844
## 45.85714 11.907527  2.326106
## 46.00000 14.369963  4.208880
## 46.14286 12.465616  1.756199
## 46.28571 16.130508  4.899498
## 46.42857 13.932828  2.203396
## 46.57143 10.454079 -2.552282
## 46.71429  7.303066 -6.558545
## 46.85714  7.697995 -6.929110
## 47.00000  8.967726 -6.313015
## 47.14286  7.172223 -8.735318
## 47.28571  9.306881 -7.203682
## 47.42857  7.323574 -9.768749
## 
## $福岡県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 45.21062 52.49830
## 45.71429 44.36376 52.97933
## 45.85714 48.10700 57.68842
## 46.00000 52.75945 62.92053
## 46.14286 52.92675 63.63617
## 46.28571 58.56227 69.79328
## 46.42857 58.24768 69.97711
## 46.57143 59.59328 72.59964
## 46.71429 59.67348 73.53509
## 46.85714 62.96051 77.58762
## 47.00000 66.69974 81.98048
## 47.14286 67.27234 83.17988
## 47.28571 71.68527 88.19583
## 47.42857 71.89991 88.99223
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 1.827484 1.827484 1.827484 1.827484 1.827484 1.827484 1.827484 1.827484
##  [9] 1.827484 1.827484 1.827484 1.827484 1.827484 1.827484
## 
## $佐賀県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%       95%
## 45.57143 -0.1974769 -1.269426
## 45.71429 -0.2703518 -1.380879
## 45.85714 -0.3407787 -1.488588
## 46.00000 -0.4089890 -1.592906
## 46.14286 -0.4751796 -1.694136
## 46.28571 -0.5395200 -1.792536
## 46.42857 -0.6021572 -1.888332
## 46.57143 -0.6632197 -1.981719
## 46.71429 -0.7228205 -2.072870
## 46.85714 -0.7810599 -2.161940
## 47.00000 -0.8380272 -2.249064
## 47.14286 -0.8938021 -2.334364
## 47.28571 -0.9484566 -2.417951
## 47.42857 -1.0020557 -2.499924
## 
## $佐賀県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 3.852445 4.924395
## 45.71429 3.925320 5.035847
## 45.85714 3.995747 5.143556
## 46.00000 4.063957 5.247875
## 46.14286 4.130148 5.349104
## 46.28571 4.194488 5.447505
## 46.42857 4.257125 5.543300
## 46.57143 4.318188 5.636687
## 46.71429 4.377789 5.727838
## 46.85714 4.436028 5.816908
## 47.00000 4.492995 5.904032
## 47.14286 4.548770 5.989332
## 47.28571 4.603425 6.072919
## 47.42857 4.657024 6.154892
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 2.977147 2.915330 3.040229 3.073910 3.015304 3.417884 3.444287 3.530268
##  [9] 3.427547 3.541730 3.547850 3.533638 3.741202 3.802715
## 
## $長崎県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                  80%        95%
## 45.57143  0.86220050 -0.2573845
## 45.71429  0.56104941 -0.6852316
## 45.85714  0.20130153 -1.3015360
## 46.00000 -0.05029516 -1.7041497
## 46.14286 -0.42834744 -2.2513066
## 46.28571 -0.29005302 -2.2529162
## 46.42857 -0.52355748 -2.6240076
## 46.57143 -0.82541317 -3.1311717
## 46.71429 -1.21282984 -3.6692969
## 46.85714 -1.40048034 -4.0167285
## 47.00000 -1.66233510 -4.4204406
## 47.14286 -1.93936228 -4.8365934
## 47.28571 -1.97865281 -5.0065612
## 47.42857 -2.15568619 -5.3098732
## 
## $長崎県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 5.092093  6.211678
## 45.71429 5.269611  6.515892
## 45.85714 5.879157  7.381994
## 46.00000 6.198116  7.851971
## 46.14286 6.458956  8.281915
## 46.28571 7.125821  9.088684
## 46.42857 7.412132  9.512582
## 46.57143 7.885950 10.191708
## 46.71429 8.067924 10.524391
## 46.85714 8.483941 11.100189
## 47.00000 8.758035 11.516141
## 47.14286 9.006638 11.903869
## 47.28571 9.461057 12.488966
## 47.42857 9.761116 12.915303
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 12.114395  9.567893  9.953163  8.989711  8.670155  7.706782  7.813739
##  [8]  9.084690 10.400804  8.114144  8.945574  9.068347  7.447984  7.329422
## 
## $熊本県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%        95%
## 45.57143  7.9563028  5.7551416
## 45.71429  4.2341783  1.4106803
## 45.85714  4.5381288  1.6715830
## 46.00000  3.2258011  0.1745713
## 46.14286  2.4694898 -0.8129444
## 46.28571  1.2872712 -2.1110125
## 46.42857  1.3094596 -2.1336976
## 46.57143  2.4014221 -1.1364861
## 46.71429  3.4208702 -0.2740837
## 46.85714  0.8436356 -3.0051390
## 47.00000  1.4456861 -2.5245145
## 47.14286  1.4174101 -2.6327510
## 47.28571 -0.3425426 -4.4665985
## 47.42857 -0.6437591 -4.8645062
## 
## $熊本県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 16.27249 18.47365
## 45.71429 14.90161 17.72511
## 45.85714 15.36820 18.23474
## 46.00000 14.75362 17.80485
## 46.14286 14.87082 18.15326
## 46.28571 14.12629 17.52458
## 46.42857 14.31802 17.76117
## 46.57143 15.76796 19.30587
## 46.71429 17.38074 21.07569
## 46.85714 15.38465 19.23343
## 47.00000 16.44546 20.41566
## 47.14286 16.71928 20.76944
## 47.28571 15.23851 19.36257
## 47.42857 15.30260 19.52335
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 2.6396822 2.0455079 0.6628872 2.1182493 2.0230491 1.9343560 1.8517252
##  [8] 1.7747425 1.7030217 1.6362031 1.5739518 1.5159554 1.4619233 1.4115843
## 
## $大分県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%        95%
## 45.57143  0.9796225  0.1008400
## 45.71429  0.1288087 -0.8858306
## 45.85714 -1.4891373 -2.6283502
## 46.00000 -0.1603536 -1.3665731
## 46.14286 -0.2681965 -1.4811087
## 46.28571 -0.3678068 -1.5864981
## 46.42857 -0.4598715 -1.6835568
## 46.57143 -0.5450115 -1.7730150
## 46.71429 -0.6237893 -1.8555287
## 46.85714 -0.6967158 -1.9316885
## 47.00000 -0.7642558 -2.0020281
## 47.14286 -0.8268328 -2.0670299
## 47.28571 -0.8848336 -2.1271316
## 47.42857 -0.9386117 -2.1827303
## 
## $大分県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 4.299742 5.178524
## 45.71429 3.962207 4.976846
## 45.85714 2.814912 3.954125
## 46.00000 4.396852 5.603072
## 46.14286 4.314295 5.527207
## 46.28571 4.236519 5.455210
## 46.42857 4.163322 5.387007
## 46.57143 4.094496 5.322500
## 46.71429 4.029833 5.261572
## 46.85714 3.969122 5.204095
## 47.00000 3.912159 5.149932
## 47.14286 3.858744 5.098941
## 47.28571 3.808680 5.050978
## 47.42857 3.761780 5.005899
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 10.70610 12.38963 12.38963 12.38963 12.38963 12.38963 12.38963 12.38963
##  [9] 12.38963 12.38963 12.38963 12.38963 12.38963 12.38963
## 
## $宮崎県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 7.888627 6.397149
## 45.71429 9.434114 7.869557
## 45.85714 9.028153 7.248694
## 46.00000 8.666194 6.695125
## 46.14286 8.336430 6.190795
## 46.28571 8.031548 5.724518
## 46.42857 7.746643 5.288793
## 46.57143 7.478237 4.878302
## 46.71429 7.223758 4.489110
## 46.85714 6.981240 4.118211
## 47.00000 6.749140 3.763244
## 47.14286 6.526220 3.422317
## 47.28571 6.311470 3.093885
## 47.42857 6.104053 2.776668
## 
## $宮崎県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 13.52357 15.01504
## 45.71429 15.34515 16.90971
## 45.85714 15.75111 17.53057
## 46.00000 16.11307 18.08414
## 46.14286 16.44283 18.58847
## 46.28571 16.74772 19.05475
## 46.42857 17.03262 19.49047
## 46.57143 17.30103 19.90096
## 46.71429 17.55550 20.29015
## 46.85714 17.79802 20.66105
## 47.00000 18.03012 21.01602
## 47.14286 18.25304 21.35695
## 47.28571 18.46779 21.68538
## 47.42857 18.67521 22.00260
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 2.561732 3.593150 3.916954 4.294517 4.294517 4.294517 4.294517 4.294517
##  [9] 4.294517 4.294517 4.294517 4.294517 4.294517 4.294517
## 
## $鹿児島県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##                 80%       95%
## 45.57143 -1.5267970 -3.691133
## 45.71429 -1.2371146 -3.794102
## 45.85714 -1.1372735 -3.812820
## 46.00000 -0.8528392 -3.577685
## 46.14286 -0.8711663 -3.605714
## 46.28571 -0.8894286 -3.633643
## 46.42857 -0.9076268 -3.661475
## 46.57143 -0.9257616 -3.689210
## 46.71429 -0.9438335 -3.716849
## 46.85714 -0.9618434 -3.744392
## 47.00000 -0.9797917 -3.771842
## 47.14286 -0.9976792 -3.799198
## 47.28571 -1.0155064 -3.826463
## 47.42857 -1.0332740 -3.853636
## 
## $鹿児島県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 6.650260  8.814597
## 45.71429 8.423414 10.980402
## 45.85714 8.971182 11.646729
## 46.00000 9.441872 12.166718
## 46.14286 9.460199 12.194747
## 46.28571 9.478462 12.222676
## 46.42857 9.496660 12.250508
## 46.57143 9.514795 12.278243
## 46.71429 9.532867 12.305882
## 46.85714 9.550876 12.333425
## 47.00000 9.568825 12.360875
## 47.14286 9.586712 12.388231
## 47.28571 9.604539 12.415496
## 47.42857 9.622307 12.442669
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##  [1] 44.74412 40.89679 39.95251 43.16165 46.78320 42.35191 44.05939 43.64454
##  [9] 43.64454 43.64454 43.64454 43.64454 43.64454 43.64454
## 
## $沖縄県$lower
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%       95%
## 45.57143 32.84213 26.541593
## 45.71429 27.94843 21.093980
## 45.85714 26.03624 18.669415
## 46.00000 28.34055 20.494735
## 46.14286 31.10941 22.812208
## 46.28571 25.86949 17.144229
## 46.42857 26.80619 17.672905
## 46.57143 24.72604 14.711192
## 46.71429 23.72359 13.178077
## 46.85714 22.76922 11.718499
## 47.00000 21.85662 10.322793
## 47.14286 20.98073  8.983242
## 47.28571 20.13746  7.693569
## 47.42857 19.32341  6.448586
## 
## $沖縄県$upper
## Time Series:
## Start = c(45, 5) 
## End = c(47, 4) 
## Frequency = 7 
##               80%      95%
## 45.57143 56.64612 62.94666
## 45.71429 53.84515 60.69959
## 45.85714 53.86878 61.23561
## 46.00000 57.98276 65.82857
## 46.14286 62.45698 70.75418
## 46.28571 58.83433 67.55960
## 46.42857 61.31258 70.44586
## 46.57143 62.56304 72.57788
## 46.71429 63.56548 74.11100
## 46.85714 64.51985 75.57058
## 47.00000 65.43245 76.96628
## 47.14286 66.30834 78.30583
## 47.28571 67.15161 79.59551
## 47.42857 67.96566 80.84049